This document is compiled from an RMarkdown file which contains all code or description necessary to reproduce the analysis for the accompanying project. Each section below describes a different component of the analysis and most of numbers and figures are generated directly from the underlying data on compilation.
LICENSE
If you want to reuse the code in this report, please note the license below should be followed.
The code is made available for non commercial research purposes only under the MIT. However, notwithstanding any provision of the MIT License, the software currently may not be used for commercial purposes without explicit written permission after contacting Shixiang Wang wangshx@shanghaitech.edu.cn or Xue-Song Liu liuxs@shanghaitech.edu.cn.
PART 0: Data preprocessing
In this part, raw data are collected from databases or papers and the core pre-processing steps are described in sections below.
The pre-processing work has been done by setting root path of the project repository as work directory. Therefore, keep in mind the work directory should be properly set if you are interested in reproducing the pre-processing procedure.
Prepare PCAWG datasets
We download PCAWG phenotype and copy number data from UCSC Xena and save them to local machine with R format.
dir.create("Xena")
# Phenotype (Specimen Centric) -----------------------------------------------
library(UCSCXenaTools)
pcawg_phenotype <- XenaData %>%
dplyr::filter(XenaHostNames == "pcawgHub") %>%
XenaScan("phenotype") %>%
XenaScan("specimen centric") %>%
XenaGenerate() %>%
XenaQuery()
pcawg_phenotype <- pcawg_phenotype %>%
XenaDownload(destdir = "Xena", trans_slash = TRUE)
phenotype_list <- XenaPrepare(pcawg_phenotype)
pcawg_samp_info_sp <- phenotype_list[1:4]
saveRDS(pcawg_samp_info_sp, file = "data/pcawg_samp_info_sp.rds")
# PCAWG (Specimen Centric) ------------------------------------------------
download.file(
"https://pcawg.xenahubs.net/download/20170119_final_consensus_copynumber_sp.gz",
"Xena/pcawg_copynumber_sp.gz"
)
pcawg_cn <- data.table::fread("Xena/pcawg_copynumber_sp.gz")
pcawg_samp_info_sp <- readRDS("data/pcawg_samp_info_sp.rds")
sex_dt <- pcawg_samp_info_sp$pcawg_donor_clinical_August2016_v9_sp %>%
dplyr::select(xena_sample, donor_sex) %>%
purrr::set_names(c("sample", "sex")) %>%
data.table::as.data.table()
saveRDS(sex_dt, file = "data/pcawg_sex_sp.rds")
pcawg_cn <- pcawg_cn[!is.na(total_cn)]
pcawg_cn$value <- NULL
pcawg_cn <- pcawg_cn[, c(1:5, 7)]
colnames(pcawg_cn)[1:5] <- c("sample", "Chromosome", "Start.bp", "End.bp", "modal_cn")
saveRDS(pcawg_cn, file = "data/pcawg_copynumber_sp.rds")Generate PCAWG sample-by-component matrix
We then read the copy number data and transform it into a CopyNumber object with R package sigminer. Then sig_tally() is used to generate the matrix for NMF decomposition.
library(sigminer)
# Only focus autosomes, suggested by Prof. Liu
pcawg_cn <- readRDS("data/pcawg_copynumber_sp.rds")
table(pcawg_cn$Chromosome)
pcawg_cn <- pcawg_cn[!Chromosome %in% c("X", "Y")]
cn_obj <- read_copynumber(pcawg_cn,
add_loh = TRUE,
loh_min_len = 1e4,
loh_min_frac = 0.05,
max_copynumber = 1000L,
genome_build = "hg19",
complement = FALSE,
genome_measure = "called"
)
saveRDS(cn_obj, file = "data/pcawg_cn_obj.rds")
# Tally -------------------------------------------------------------------
library(sigminer)
cn_obj <- readRDS("data/pcawg_cn_obj.rds")
table(cn_obj@data$chromosome)
tally_X <- sig_tally(cn_obj,
method = "X",
add_loh = TRUE,
cores = 10
)
saveRDS(tally_X, file = "data/pcawg_cn_tally_X.rds")
str(tally_X$all_matrices, max.level = 1)
p <- show_catalogue(tally_X,
mode = "copynumber", method = "X",
style = "cosmic", y_tr = function(x) log10(x + 1),
y_lab = "log10(count +1)"
)
p
ggplot2::ggsave("output/pcawg_catalogs_tally_X.pdf", plot = p, width = 16, height = 2.5)
head(sort(colSums(tally_X$nmf_matrix)), n = 50)
## classes without LOH labels
tally_X_noLOH <- sig_tally(cn_obj,
method = "X",
add_loh = FALSE,
cores = 10
)
saveRDS(tally_X_noLOH$all_matrices, file = "data/pcawg_cn_tally_X_noLOH.rds")
str(tally_X_noLOH$all_matrices, max.level = 1)
p <- show_catalogue(tally_X_noLOH,
mode = "copynumber", method = "X",
style = "cosmic", y_tr = function(x) log10(x + 1),
y_lab = "log10(count +1)"
)
p
ggplot2::ggsave("output/pcawg_catalogs_tally_X_noLOH.pdf", plot = p, width = 16, height = 2.5)The generated catalog profiles for LOH version and non-LOH version component classification can be viewed by the following link:
- pcawg_catalogs_tally_X.pdf - 176 components
- pcawg_catalogs_tally_X_noLOH.pdf - 136 components
Prepare TCGA datasets
TCGA allele-specific copy number data are downloaded from GDC portal and transformed into R format by Huimin Li.
The data is go further checked and cleaned by Shixiang.
# Huimin collected TCGA data from GDC portal
# and generate file with format needed by sigminer
# Here I will firstly further clean up the data
library(data.table)
x <- readRDS("data/TCGA/datamicopy.rds")
setDT(x)
colnames(x)[6] <- "minor_cn"
head(x)
x[, sample := substr(sample, 1, 15)]
saveRDS(x, file = "data/TCGA/tcga_cn.rds")
rm(list = ls())TCGA phenotype data is downloaded from UCSC Xena.
download.file(
"https://pancanatlas.xenahubs.net/download/Survival_SupplementalTable_S1_20171025_xena_sp.gz",
"Xena/Survival_SupplementalTable_S1_20171025_xena_sp.gz"
)
tcga_cli <- data.table::fread("Xena/Survival_SupplementalTable_S1_20171025_xena_sp.gz")
table(tcga_cli$`cancer type abbreviation`)
saveRDS(tcga_cli, file = "data/TCGA/tcga_cli.rds")Generate TCGA sample-by-component matrix
Similar to PCAWG, TCGA matrices are also generated.
library(sigminer)
# Only focus autosomes
tcga_cn <- readRDS("data/TCGA/tcga_cn.rds")
table(tcga_cn$chromosome)
tcga_cn <- tcga_cn[!chromosome %in% c("chrX", "chrY")]
cn_obj <- read_copynumber(
tcga_cn,
seg_cols = c("chromosome", "start", "end", "segVal"),
add_loh = TRUE,
loh_min_len = 1e4,
loh_min_frac = 0.05,
max_copynumber = 1000L,
genome_build = "hg38",
complement = FALSE,
genome_measure = "called"
)
saveRDS(cn_obj, file = "data/TCGA/tcga_cn_obj.rds")
# Tally step
# Generate the counting matrices
# Same as what have done for PCAWG dataset
library(sigminer)
cn_obj <- readRDS("data/tcga_cn_obj.rds")
table(cn_obj@data$chromosome)
tally_X <- sig_tally(cn_obj,
method = "X",
add_loh = TRUE,
cores = 10
)
saveRDS(tally_X, file = "data/TCGA/tcga_cn_tally_X.rds")
p <- show_catalogue(tally_X,
mode = "copynumber", method = "X",
style = "cosmic", y_tr = function(x) log10(x + 1),
y_lab = "log10(count +1)"
)
p
ggplot2::ggsave("output/tcga_catalogs_tally_X.pdf", plot = p, width = 16, height = 2.5)
head(sort(colSums(tally_X$nmf_matrix)), n = 50)
## classes without LOH labels
tally_X_noLOH <- sig_tally(cn_obj,
method = "X",
add_loh = FALSE,
cores = 10
)
saveRDS(tally_X_noLOH$all_matrices, file = "data/TCGA/tcga_cn_tally_X_noLOH.rds")
str(tally_X_noLOH$all_matrices, max.level = 1)
p <- show_catalogue(tally_X_noLOH,
mode = "copynumber", method = "X",
style = "cosmic", y_tr = function(x) log10(x + 1),
y_lab = "log10(count +1)"
)
p
ggplot2::ggsave("output/tcga_catalogs_tally_X_noLOH.pdf", plot = p, width = 16, height = 2.5)The generated catalog profiles for LOH version and non-LOH version component classification can be viewed by the following link:
- tcga_catalogs_tally_X.pdf - 176 components
- tcga_catalogs_tally_X_noLOH.pdf - 136 components
Other PCAWG genotype/phenotype data
Most of other PCAWG genotype/phenotype data used in this study are obtained from PCAWG paper collection published in early 2020.
TODO: describe detailed datasets.
PART 1: Copy number signature identification and activity attribution
In this part, how the copy number signatures are identified and how the corresponding signature activities (a.k.a. exposures) are attributed are described in sections below. The 176 component classification for PCAWG data is our main focus.
The work of this part has been done by either setting root path of the project repository as work directory or moving data and code to the same path (see below). Therefore, keep in mind the work directory/data path should be properly set if you are interested in reproducing the analysis procedure.
The signature identification work cannot be done in local machine because high intensity calculations are required, we used HPC of ShanghaiTech University to submit computation jobs to HPC clusters. Input data files, R scripts and PBS job scripts are stored in a same path, the structure can be viewed as:
.
├── 04-call-tcga-signatures-with-BP-types.R
├── 05-call-pcawg-signatures-with-BP-types.R
├── bp_pcawg.pbs
├── bp_pcawg_types.pbs
├── bp_tcga.pbs
├── bp_tcga_types.pbs
├── call_pcawg_bp.R
├── call_pcawg_sp.R
├── call_tcga_bp.R
├── call_tcga.R
├── pcawg_cn_tally_X.rds
├── pcawg.pbs
├── tcga_cn_tally_X.rds
├── tcga.pbsCopy number signature identification
The signature identification pipeline is adopted from Degasperi et al. (2020), we implemented the procedure in our package sigminer by following the method description. A benchmark has been done to make sure our implementation works properly.
Identification pipeline
To make the signature identification procedure more clear, we drew a flowchart. We name this pipeline “Best Practice” and use its short name BP (or bp).
TODO: add flowchart
Apply to PCAWG catalog matrix
PBS file bp_pcawg.pbs content:
#PBS -l walltime=500:00:00
#PBS -l nodes=1:ppn=50
#PBS -S /bin/bash
#PBS -l mem=300gb
#PBS -j oe
#PBS -M w_shixiang@163.com
#PBS -q slst_pub
# Please set PBS arguments above
cd /public/home/wangshx/wangshx/PCAWG-TCGA
module load apps/R/3.6.1
# Following are commands/scripts you want to run
Rscript call_pcawg_bp.RR script call_pcawg_bp.R content:
library(sigminer)
tally_X <- readRDS("pcawg_cn_tally_X.rds")
tally_X_noLOH <- readRDS("pcawg_cn_tally_X_noLOH.rds")
sigs <- bp_extract_signatures(
tally_X$nmf_matrix,
range = 2:30,
n_bootstrap = 10,
n_nmf_run = 10,
cores = 50,
cache_dir = "pcawg_bp_pynmf",
keep_cache = TRUE,
cores_solution = 30,
pynmf = TRUE,
use_conda = TRUE
)
saveRDS(sigs, file = "pcawg_cn_sigs_CN176_BP.rds")Signature number determination
The code above call 100 NMF runs (10 bootstrap catalogs and 10 NMF runs for each bootstrap catalog) for each signature number. Only 1000 NMF runs (20 bootstrap catalogs and 50 NMF runs for each bootstrap catalog) was applied for 176-component classification approach, which has the same arguments proposed by Degasperi et al. (2020).
First, load the results.
library(sigminer)
solution1000 <- readRDS("../BP/BP_PCAWG_1000_Extraction_Result.rds")
solution100 <- readRDS("../data/pcawg_cn_sigs_CN176_BP.rds")Next we compare some important measures for signature number determination. We can see 100 NMF runs and 1000 NMF runs can achieve very similar results.
bp_show_survey(solution1000, add_score = F, fixed_ratio = F)
bp_show_survey(solution100, add_score = F, fixed_ratio = F)
Basically, the 5 measures can be classified into 3 categories:
silhouetteandsimilarity: these two scores indicate the result stability. The signature number with value decreases sharply is preferred.distanceanderror: these two scores indicate the difference between raw matrix and reconstructed matrix for signatures. The signature number with lower value is better.pos cor: this is constructed by Prof. Liu and Shixiang to see the average positive correlation between signatures. A signature number should be considered if it has lower value. In practice, we find this measure has referential value sometimes.
More see ?bp_show_survey in R console for how they are calculated.
In previous study, Degasperi et al. (2020) used silhouette and error for signature number determination, PCAWG Mutational Signatures Working Group et al. (2020) used similarity and distance for signature number determination. For plots above we can see that they very close.
From plots above, we observe that the stability drops sharply after 11, so here 11 signatures is selected for this PCAWG dataset.
Similarity between two results with different parameter setting
We have two results above, so we can compare the signatures from two different parameter setting by calculating their pairwise cosine similarity.
sim <- get_sig_similarity(solution1000$object$K11, solution100$object$K11)
ComplexHeatmap::pheatmap(sim$similarity, cluster_rows = FALSE, cluster_cols = FALSE, display_numbers = TRUE)
We can know that the results contain same signature profile! This practice indicates that 100 NMF runs for each signature number is enough.
The Signature object with 11 signature profiles are stored for further analysis.
# Use root directory as work directory
saveRDS(solution1000$object$K11, file = "data/pcawg_cn_sigs_CN176_signature.rds")Signature activity attribution
After signature determination, the activities (a.k.a. exposures) of signatures are attributed to each sample by following a step-wise approach, which adopted from PCAWG Mutational Signatures Working Group et al. (2020). Of note, the approach is simplified by keeping the implementation of core ideas but discarding the useless steps due to few prior knowledge about copy number signatures.
Attribution pipeline
There are two core steps in this pipeline:
- step1: initialize a signature set (signatures observed in a cancer type the sample belongs to) for a sample and remove useless signature contribution by setting 0 to a sample if has none or very few contribution (cosine similarity reduces <0.1) to reconstruct the raw catalog from signatures.
- step2: add a signature from global signatures if if promote the catalog reconstruction (cosine similarity increases >0.5).
TODO: add flowchart
Apply to PCAWG catalog matrix and signatures
We apply the attribution pipeline above.
# Use root directory as work directory
library(sigminer)
tally_X <- readRDS("data/pcawg_cn_tally_X.rds")
pcawg_types <- readRDS("data/pcawg_type_info.rds")
sc <- pcawg_types$cancer_type
names(sc) <- pcawg_types$sample
pcawg_expo <- bp_attribute_activity(
solution1000$object$K11,
sample_class = sc,
nmf_matrix = tally_X$nmf_matrix,
return_class = "data.table"
)
saveRDS(pcawg_expo, file = "data/pcawg_cn_sigs_CN176_activity.rds")Now we can know how well each sample catalog profile can be constructed from the signature combination.
pcawg_expo <- readRDS("../data/pcawg_cn_sigs_CN176_activity.rds")
summary(pcawg_expo$similarity) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.4462 0.8651 0.9170 0.9075 0.9681 0.9999 hist(pcawg_expo$similarity, breaks = 100, xlab = "Reconstructed similarity", main = NA)
Most of samples are well constructed. Next we will use a similarity threshold 0.75 to filter out some samples for removing their effects on the following analysis.
TCGA copy number signatures
To validate the copy number signatures discovered in PCAWG database, we need another database for validation. TCGA is the best resource we got and similar processing and signature identification are applied.
library(sigminer)
tally_X <- readRDS("tcga_cn_tally_X.rds")
tally_X_noLOH <- readRDS("tcga_cn_tally_X_noLOH.rds")
sigs <- bp_extract_signatures(
tally_X$nmf_matrix,
range = 2:30,
cores = 50,
n_bootstrap = 10,
n_nmf_run = 10,
cache_dir = "tcga_bp_pynmf",
keep_cache = TRUE,
cores_solution = 30,
pynmf = TRUE,
use_conda = TRUE
)
saveRDS(sigs, file = "tcga_cn_sigs_CN176_BP.rds")Load the solution list:
tcga_solutions <- readRDS("../data/TCGA/tcga_cn_sigs_CN176_BP.rds")bp_show_survey(tcga_solutions, add_score = FALSE, fixed_ratio = FALSE, )
Also 11 signatures are determined based on survey plot.
Similarly, we apply the attribution pipeline to TCGA.
library(sigminer)
tcga_solutions <- readRDS("data/TCGA/tcga_cn_sigs_CN176_BP.rds")
tcga_expo <- bp_attribute_activity(
tcga_solutions$object$K11,
sample_class = sc,
nmf_matrix = tally_X$nmf_matrix,
return_class = "data.table"
)
saveRDS(tcga_expo, file = "data/TCGA/tcga_cn_sigs_CN176_activity.rds")tcga_expo <- readRDS("../data/TCGA/tcga_cn_sigs_CN176_activity.rds")
summary(tcga_expo$similarity) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.2697 0.8464 0.8994 0.8878 0.9410 0.9933 hist(tcga_expo$similarity, breaks = 100, xlab = "Reconstructed similarity", main = NA)
PART 2: Signature profile visualization and analysis
In this part, we would like to visualize and analyze all tumor samples as a whole.
Signature profile visualization
library(tidyverse)
library(sigminer)
pcawg_sigs <- readRDS(file = "../data/pcawg_cn_sigs_CN176_signature.rds")Show signature profile with relative contribution value for each component.
show_sig_profile(
pcawg_sigs,
mode = "copynumber",
method = "X",
style = "cosmic"
)
Show signature profile with absolute contribution value (estimated segment count) for each component.
show_sig_profile(
pcawg_sigs,
mode = "copynumber",
method = "X",
style = "cosmic",
normalize = "raw"
)
Filter out samples with low reconstructed level (similarity < 0.75).
pcawg_expo <- readRDS("../data/pcawg_cn_sigs_CN176_activity.rds")
keep_samples <- pcawg_expo$similarity >= 0.75
pcawg_expo$abs_activity <- pcawg_expo$abs_activity[keep_samples]
pcawg_expo$rel_activity <- pcawg_expo$rel_activity[keep_samples]
expo_mat <- pcawg_expo$abs_activity %>%
tibble::column_to_rownames("sample") %>%
as.matrix() %>%
t()Show signature activity profile.
show_sig_exposure(
expo_mat,
style = "cosmic",
rm_space = TRUE,
palette = sigminer:::letter_colors
)
Signature analysis
Signature similarity
Here we explore the similarity between copy number signatures identified in PCAWG data. High cosine similarity will cause some issues in signature assignment (Maura et al. 2019).
sim <- get_sig_similarity(pcawg_sigs, pcawg_sigs)pheatmap::pheatmap(sim$similarity, cluster_cols = F, cluster_rows = F, display_numbers = TRUE)
Most of signature-pairs have similarity around 0.1.
Signature activity distribution
PART 3: Pan-Cancer Signature Distribution
PART 4: Cancer subtyping and prognosis analysis
PART 5: Inference of copy number signature etiologies
Supplementary Analyses
R Session:
Many thanks to authors and contributors of all packages used in this project.
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.2 (2020-06-22)
os CentOS Linux 7 (Core)
system x86_64, linux-gnu
ui X11
language EN
collate en_US.UTF-8
ctype en_US.UTF-8
tz Asia/Shanghai
date 2020-12-14
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[1] /home/public/R/library
[2] /opt/microsoft/ropen/4.0.2/lib64/R/library